Sifting my Dunbar number out of siloed data

You’ve probably heard more than you care to about the Dunbar number – the idea in sociology that you cannot maintain more than 150 real friendships. I began to wonder about myself. How many people do I really contact in a given period of time – say a month? This started me on revealing a journey into my own social graph.

Your social graph is all of the people that you are connected to online. I keep profiles on a number of social sites, but I only really keep up my LinkedIn, Facebook and Twitter accounts. What I realized is that many of the people that I talk to day-in and day-out are not on these sites – or at least we don’t use them to stay in touch. Perhaps this is a function of my age (>40), or some other factor, but my curiosity grew regarding the Dunbar number, how it related to me and how it might impact us all as our number of “friends” (i.e. our social graph) continues to grow. So, I decided to turn the analysis around on myself.

My first task (and problem) was how to find my own Dunbar number?

How do I connect to the people that I know and talk to on a regular basis? That part was easy. I email, talk on the phone and text (in rank order). What’s not easy is getting to the data. Well, getting to the data was easy because my phone bills are online, and so are all my emails, but porting the data is another issue. It cannot be exported into any form that allows me to analyze it. You have to get it all by hand. So, after a little cut-and-paste I had my calls and texts in Excel, but it was just a long line of dates, times, phone numbers, lengths of calls and total messages sent.

Isn’t this my data? Shouldn’t it be easier for me to analyze? Wouldn’t the phone company be doing me a service by at least allowing me to pull the feed into some kind of analytic tools ala Mint (if they are not going to serve me by looking at it for me and offering better products and services to meet my needs)? But I digress…literally back into my useless data set.

Faced with a useless mass of data, I needed to cull it down, sift and sort it, and then find an apples-to-apples way to compare the people I call the most (quantitative) and to factor in the way that I am contacting them (qualitative).

So, I created my own personal Dunbar Index.

I looked in detail at my phone calls and consolidated them down to how many calls I made/received and the total minutes of talk time. I also logged how many text messages that I send. Because I call more people than I text, texting needed to rank differently. In fact, I only text a certain people that I know better, so I needed to weight the texts more heavily. I chose an arbitrary 2.5 factor. Next, I created a very simple index out of the two numbers and matched them to my phone list. I made the actual numbers anonymous and then charted them.

Click on the charts to enlarge them.

Total Call/Text Index

Dunbar Graph sans top 3

You can see in the first one that the index has a long tail. Out of 80-ish phone calls and texts, only less than 10 make up the bulk of the volume. Even when I factor out the top 3 (second chart) because they are my spouse/home and my Google Voice voice mail and Calendar contacting me, you can see that the bulk of my calls are under 30 minutes for the month (the red line). I was surprised how many total people that I called, and how much I talked to just a few people. But this is only part of the story.

What about email?

I am a gmail user, and I love the service, but I cannot figure out an automated way to count how many emails that I sent and to whom during these same 30 days besides just counting by hand. And I’ve looked. If anyone has any ideas about how to do this, I’m all ears. Otherwise, I’ll try and hand count (it’s just a 30 day period), and then factor it into my index. This would give me realistic look at my own Social Graph using the media that I prefer to communicate with them.

So, how does this compare to my online Social Graph? And what (if anything) does this mean?

As I mentioned, I keep up my profiles and contacts via LinkedIn (tends to be more professional), Facebook (tends to be more personal), Twitter (tends to be…random), and now Buzz (just a few people). But my use of these sites is different, and has left me wondering how it integrates into my daily communication habits and patterns. And this has me wondering about all of the people that my clients are trying to reach about their varied products and services.

Markets diverge. Plain and simple. There will not be a universal device. You are going to have 4 and then 5 and then 6 screens in your life with apps and data running on all of them. Getting to all of your data – it is yours and mine after all – but getting to your data and porting it to other services (like Mint or Trip-It for example) will be important in order to realize all the possibilities that the ecosystem of networked screens affords to you.

My data is stored across so many different databases and interfaces that it is almost impossible for me to consolidate all of it. All of our data is this way. It is locked away in multiple different locations, which makes any kind of macro-level analysis impossible or incredibly labor intensive. Imagine trying to do this with 100K names or 100MM. This is a big challenge for marketers today, and it is only going to get worse.

As more sites spring up asking us to create profiles we just keep perpetuating the problem; compounding it. The freedom of this data, or at least the portability of it is a key feature of the much promised, but yet-to-be-realized Web 3.0. The web of data that can be ported, sifted, sorted, repurposed and mashed-up into all kinds of useful ways is right now just a pipe dream. But I think we are all hoping for it, and looking forward not to the freedom of our data, but the benefits that it will give to us from financial analysis and truly virtual assistants to things that will not be imagined until this future becomes just a bit more real.